An End-to-End Pipeline for Causal ML with Continuous Treatments: An Application to Financial Decision Making

arXiv:2609.30396 · stat.ME, cs.LG, stat.AP, stat.ML · Submitted 2026-09-24 · Read on arXiv

stat.ME, cs.LG, stat.AP, stat.ML

Submitted: 2026-09-24

Updated: 2026-09-24

Code: https://github.com/javiermoralh/causal-pipeline

Terminology

• Causal Inference — 18× in this paper · explained in 4 episodes
• Causal Discovery — 10× in this paper · explained in 2 episodes
• Robustness — 6× in this paper · explained in 5 episodes
• Data Augmentation — 5× in this paper · explained in 7 episodes
• Feature Selection — 5× in this paper · explained in 4 episodes
• AUC — 3× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Ablation study — 3× in this paper · explained in 1 episode
• Synthetic Data Generation — 3× in this paper · explained in 5 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Adjustment Sets — 2× in this paper · explained in 1 episode
• Sparsity — 2× in this paper · explained in 4 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Causal Reasoning — 1× in this paper · explained in 2 episodes
• Causal Understanding — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Diagnostic Tools — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Greedy Equivalence Search — 1× in this paper
• Heteroscedasticity — 1× in this paper · explained in 2 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Methodological Rigor — 1× in this paper · explained in 2 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Normalizing Flow — 1× in this paper · explained in 3 episodes
• Normalizing Flows — 1× in this paper · explained in 4 episodes
• Personalization — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes
• Sequential Forward Selection — 1× in this paper
• Temporal consistency — 1× in this paper · explained in 2 episodes

Sources

• CausalML: Python Package for Causal Machine Learning
• High Dimensional Causal Inference with Variational Backdoor Adjustment
• DoWhy: An End-to-End Library for Causal Inference

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